Advances in Cardiac Troponin Composition Assays: A Step Closer to the Clinic?

IF 7.1 2区 医学 Q1 MEDICAL LABORATORY TECHNOLOGY Clinical chemistry Pub Date : 2024-12-19 DOI:10.1093/clinchem/hvae206
Xander M R van Wijk, Sander A J Damen
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来源期刊
Clinical chemistry
Clinical chemistry 医学-医学实验技术
CiteScore
11.30
自引率
4.30%
发文量
212
审稿时长
1.7 months
期刊介绍: Clinical Chemistry is a peer-reviewed scientific journal that is the premier publication for the science and practice of clinical laboratory medicine. It was established in 1955 and is associated with the Association for Diagnostics & Laboratory Medicine (ADLM). The journal focuses on laboratory diagnosis and management of patients, and has expanded to include other clinical laboratory disciplines such as genomics, hematology, microbiology, and toxicology. It also publishes articles relevant to clinical specialties including cardiology, endocrinology, gastroenterology, genetics, immunology, infectious diseases, maternal-fetal medicine, neurology, nutrition, oncology, and pediatrics. In addition to original research, editorials, and reviews, Clinical Chemistry features recurring sections such as clinical case studies, perspectives, podcasts, and Q&A articles. It has the highest impact factor among journals of clinical chemistry, laboratory medicine, pathology, analytical chemistry, transfusion medicine, and clinical microbiology. The journal is indexed in databases such as MEDLINE and Web of Science.
期刊最新文献
Optimal Cutoff Values and Utility of High-Sensitivity Troponin T and NT-proBNP for the Risk Stratification of Patients with Acute Pulmonary Embolism Advances in Cardiac Troponin Composition Assays: A Step Closer to the Clinic? Design and Analytical Evaluation of Novel Cardiac Troponin Assays Targeting Multiple Forms of the Cardiac Troponin I-Cardiac Troponin T-Troponin C Complex and Fragmentation Forms. Correction to: Prospective and External Validation of an Ensemble Learning Approach to Sensitively Detect Intravenous Fluid Contamination in Basic Metabolic Panels. An AI Model (LORIS) to Predict Immune Checkpoint Blockade Response in Cancer: A Clinical Data Science Perspective.
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